KMK Backend

This repository contains the backend part of the KMK project

Demo

Watch it on Youtube

Getting started

Pre-commit hooks

After you cloned the repository, make sure that you have pre-commit package installed. To install it globally run the following code

$ pip3 install pre-commit==2.13.0

Then, to setup pre-commit hooks run this code in the root of the working directory

$ pre-commit install

Running locally

To run the project on your local machine simply execute the following code

$ docker-compose up --build

Then, you can see the API documentation on http://127.0.0.1:5000/docs

Testing

To run the tests execute the following code

$ scripts/run_tests.sh

Requirements

Stakeholder's Roles

RUP Deliverable

Design

In this project, we follow the design patterns of Domain Driven Development. The domains are User, Lobby, and Statistics; on top of them, there are three corresponding repositories that are responsible for interacting with the DB. The API, in turn, uses services that proxy the interaction between the Fast API client and repositories.

Architecture

Abstract

The architecture of the project is typical for a web application. In the middle, there is a RESTful API backend, that can be used by different clients: i.g., a web client, a telegram bot, Postman, etc.

The interaction with a third-party image hosting service is prototyped. Although, it can can be implemented on a client, since the backend accepts images in form of urls for them.

Static View

Dynamic View

Code linters and Tests

Pre-commit hooks

Before commiting the changes we've made, it is always beneficial to double-check whether there are any issues in the new code, so we use isort and flake8 checkers for this purpose.

For example, a pre-commit report might look like this

Linters

Linting is the first stage of the Github Actions CI pipeline in this repository. The same linters, isort and flake8, are used to check the quality of the last pushed code to a branch.

After the pipeline is finished, the report is uploaded to Codacy, which yields code quality dashboards about our repository

Coverage

Tests are ran whenever new code is pushed to the remote; they help us to ensure that the code works as intended, and that no new bugs appeared in our codebase.

After the pipeline is finished, the coverage report is uploaded to codecov, where a developer may find the visuals on tests coverage in the repository.

Also, the Codecov Bot assists with coverage information when a Pull Request is reviewed